Method and system for determining on-line influence in social media

ABSTRACT

Methods and system are provided for determining a topical influence value of the individual based on aggregated viral properties of tagged content citing the individual. A processor of a computer is used to match content within a web-site with a selected topic and to tag matching content to generate tagged content. Viral properties for the tagged content are extracted, and viral properties of the tagged content citing an individual in the tagged content are aggregated to form aggregated viral properties of the tagged content citing the individual. Based on the aggregated viral properties of the tagged content citing the individual, a topical influence value of the individual can be computed.

RELATED APPLICATIONS

The present application is a Continuation of the U.S. application Ser.No. 12/437,418, filed May 7, 2009, which is a Continuation-in-Part (CIP)of the U.S. application Ser. No. 12/174,345 filed Jul. 16, 2008 entitled“Method and System for Determining Topical On-Line Influence of anEntity”, and claims benefit from the US provisional application toChristopher NEWTON Ser. No. 61/051,202 filed on May 7, 2008 entitled“Method and System for Determining On-Line Influence in Social Media”,and the U.S. application Ser. No. 12/174,345 filed Jul. 18, 2008entitled “Method and System For Determining Topical On-Line Influence ofan Entity”, both of which are incorporated herein by reference.

FIELD OF THE INVENTION

The present patent application relates to a computer implemented methodand system for determining on-line influence in social media, and inparticular, to a computer implemented method and system for determiningon-line influence of a commentor, an individual, or a web-site.

BACKGROUND OF THE INVENTION

Determining on-line influence of a commentor, an individual, an entity,or a web-site, also to be referred herein as site, in social mediabecomes an increasingly important subject nowadays. A major problemfacing marketers and public relation (PR) professionals revolves aroundthe prolific use of social media sites and the awesome scale they haveachieved. Literally, hundreds of thousands of videos, blog posts,podcasts, events, and social network interactions, such as wall posts,group postings, and others, occur daily. Due to the sheer volume ofcontent, constantly changing landscape of popular sites, and hundreds ofmillions of users involved, it is impossible to determine who should belistened to and those who must be engaged.

Existing systems for determining influence in social media are sitebased, i.e. their models of influence are calculated on a per-sitebasis. If there is a one-to-one relationship between a site and a person(an author), then the influence is extrapolated to indicate theinfluence of the person for the medium, in which the site exists. Forinstance, if siteA is a blog with only one author, and all blogs arecounted similarly, then the influence for siteA, as calculated by theprior art methods, would also indicate the influence for the author ofthe blog.

To the best of the knowledge of inventors, prior art methodspredominantly calculate influence in social media by recursivelyanalyzing inbound web page link counts. For example, siteA would have ahigher influence score than siteB if the following approximate rulesapply:

RULE 1: If the number of sites having links pointing at siteA is higherthan the number of sites having links pointing at siteB; andRULE 2: If the count of sites pointing at the sites that point at siteAis higher than the count of sites that are pointing at the sites thatpoint at siteB.

Rule 2 is applied recursively.

Various issues exist with this prior art method, namely:

-   -   The method assumes that the total influence of the sites can be        measured by a single property and that no other factors affect        influence to a scale large enough to invalidate using only        inbound link count as the measured property;    -   The method assumes that the link graph representing all the        links between the sites is complete enough to form a basis for        determining an influence;    -   The method assumes that a link implies that the linker has been        influenced by the site he is linking to, which is not        necessarily the case;    -   The method does not account for connections someone may have        with a site, if there is no link to track that connection, i.e.        if a visitor does not own a blog, and therefore does not link        out to anyone, but he is still a frequent visitor to the blog,        e.g., http://www.autoblog.com, then the influence that Autoblog        has over the visitor is not calculated; and    -   The method does not map properly to other types of content and        methods of social media expression, e.g., link-analysis methods        deployed to the blogosphere are not relevant in the micromedia        sphere of Twitter, i.e. link analysis techniques do not        translate to all forms of social media and therefore they leave        out entire pools of influencers that use other media channels as        their voice.

Accordingly, there is a need in the industry for developing alternativeand improved methods and system for determining on-line influence ofindividuals and commentors cited or published in social media content aswell as for determining the influence of web-sites hosting the content.

SUMMARY OF THE INVENTION

There is an object of the invention to provide an improved method andsystem for determining topical on-line influence of a commentor, anindividual, or a site, which would avoid or mitigate the above mentioneddrawbacks of the prior art.

According to the embodiments of the invention, a topical on-lineinfluence is introduced, which is a measure of how many people areengaged in a message of an entity content site around a given topic.Additionally the influence can also be a measure of the popularity levelof a commentor, or an individual, or how influential they are around agiven topic.

A user is allowed to manipulate equations by adding additional viralproperties to the equations or removing certain viral properties fromthe equations, and by adjusting weights in the equations.

According to one aspect of the invention, there is provided a method fordetermining a topical on-line influence of a commentor, the methodcomprising:

-   -   (a) matching and tagging content with a selected topic to        generate tagged content;    -   (b) extracting viral properties for the tagged content;    -   (c) identifying a commentor from said tagged content; and    -   (d) determining the topical on-line influence of said commentor;        wherein said topical on-line influence of said commentor is        characterized by a topical influence value of the commentor; and        said topical influence value of the commentor is calculated from        a commentor influence model based on the extracted viral        properties.

Additionally, the commentor influence model is a linear combination ofthe extracted viral properties weighted with respective weights appliedto each of the extracted viral properties.

Furthermore, step (d) of the method further comprises:

-   -   determining topical influences of on-line outlets owned by said        commentor said topical influences characterized by respective        topical influence values of the on-line outlets; and    -   creating an updated commentor influence model by forming a        linear combination of the topical influence values of said        on-line outlets and the topical influence value of the        commentor.

Beneficially, each topical influence of the topical influences of theon-line outlets owned by said commentor is determined from a linearcombination of viral properties extracted from a corresponding on-lineoutlet.

In one modification, the method further comprises:

-   -   (e) aggregating the viral properties of tagged content contained        within a web-site and having a comment from said commentor to        form aggregated viral properties of the tagged content within        the web-site; and    -   (f) calculating a topical influence value of the web-site based        on a linear combination of the aggregated viral properties of        the tagged content within the web-site and the topical influence        value of the commentor.

In a further modification, the method comprises:

-   -   (g) identifying an individual cited in the tagged content        contained within the web-site;    -   (h) aggregating viral properties of the tagged content citing        said individual to form aggregated viral properties of the        tagged content citing said individual; and    -   (i) calculating an influence value of said individual based on a        linear combination of the aggregated viral properties of the        tagged content citing said individual.

In another modification, the method further comprises:

-   -   (j) updating the topical influence value of the web-site by        calculating the topical influence value of the web-site based on        a linear combination of the aggregated viral properties of the        tagged content within the web-site, the topical influence value        of the commentor and the influence value of said individual.

Step (i) further comprises:

-   -   (i) identifying an outlet owned by said individual;    -   (ii) calculating an influence value of said outlet owned by said        individual based on a linear combination of viral properties        extracted from said outlet owned by said individual; and    -   (iii) updating the influence value of said individual based on a        linear combination of said aggregated viral properties of the        tagged content citing said individual and the influence value of        said outlet owned by said individual.

Step (b) of the method comprises:

-   -   collecting values of the viral properties at predetermined time        intervals; and    -   storing the collected values in respective time series.

Furthermore, the viral properties are selected from the group consistingof:

-   -   user engagement value; average comment count; average unique        commentor count;    -   cited individual count; inbound links; subscribers; average        social bookmarks;    -   average social news votes; buries; total count of posts; and        total count of appearance of Individuals names across all posts.

According to another aspect of the invention, a method for determining atopical on-line influence of a web-site is disclosed. The methodcomprises:

-   -   (a) matching and tagging content with a selected topic to form        tagged content;    -   (b) extracting viral properties for the tagged content;    -   (c) aggregating the viral properties across all tagged content        contained within the web-site; and    -   (d) calculating an influence value of the web-site based on a        linear combination of the aggregated viral properties, said        influence value characterizing the topical on-line influence of        the web-site.

The method further comprises:

-   -   (e) identifying a commentor having a comment in said tagged        content contained within the web-site;    -   (f) determining a topical influence of said commentor wherein        said topical influence is characterized by an influence value of        the commentor and said influence value of the commentor is        calculated based on a linear combination of the aggregated viral        properties of the tagged content contained within the web-site        and having the comment from said commentor; and    -   (g) updating the topical on-line influence of the web-site by        integrating the influence value of the commentor in the        calculation of the influence value of the web-site.

Additionally, the method further comprises:

-   -   (h) identifying an individual cited in said tagged content        contained within the web-site;    -   (i) determining a topical influence of said individual        characterized by an influence value of the individual and said        influence value of the individual is calculated based on a        linear combination of the aggregated viral properties of the        tagged content contained within the web-site and citing said        individual; and    -   (j) updating the topical on-line influence of the web-site by        integrating the influence value of the commentor and the        influence value of the individual in the calculation of the        influence value of the web-site.

The step of calculating the influence value of said commentor comprises:

-   -   (i) identifying an outlet owned by said commentor;    -   (ii) determining an influence of said outlet characterized by an        influence value of said outlet; and    -   (iii) calculating the influence value of said commentor by        including the influence value of said outlet in the linear        combination of the aggregated viral properties of the tagged        content contained within the web-site and having a comment from        said commentor.

The step of calculating the influence value of said individualcomprises:

-   -   (i) identifying an outlet owned by said individual;    -   (ii) determining an influence of said outlet characterized by an        influence value of said outlet; and    -   (iii) calculating the influence value of said individual by        including the influence value of said outlet in the linear        combination of the aggregated viral properties of the tagged        content contained within the web-site and citing said        individual.

In one modification, the method further comprises iterating steps (a) to(d) for a plurality of web-sites and identifying top influential sitesbased on the influence value of each of the plurality of web-sites.

In another modification, the method further comprising iterating steps(e) to (g) for a plurality of commentors and identifying top influentialcommentors based on the influence value of each of the plurality ofcommentors.

In yet another modification, the method further comprises iteratingsteps (h) to (j) for a plurality of individuals and identifying topinfluential individuals based on the influence value of each of theplurality of individuals.

In yet another aspect of the invention, a computer readable medium isdisclosed. The computer readable medium comprises a computer codeinstructions stored thereon, which, when executed by a computer, performthe steps of the methods of the present invention.

In a further aspect, a system for determining a topical on-lineinfluence of a web-site is disclosed. The system comprises:

-   -   a processor; and        a computer readable storage medium having computer readable        instructions stored thereon for execution by the processor,        forming the following modules:    -   (a) a matching module for matching and tagging content to a        selected topic to generate tagged content;    -   (b) a viral properties extraction module for extracting viral        properties of said tagged content;    -   (c) a commentor influence modeling module for calculating an        influence value of a commentor based on viral properties        extracted from tagged content having a comment from the        commentor, said influence value of the commentor characterizing        a topical influence of the commentor; and    -   (d) a site influence modeling module for calculating an        influence value of a web-site based on viral properties        extracted from tagged content contained in said web-site, said        influence value of the web-site characterizing a topical        influence of the web-site.

The site influence modeling module further comprises a site influenceupdating module for updating the influence value of the web-site basedon said viral properties extracted from the tagged content contained insaid web-site, an influence value of a commentor having a comment insaid tagged content and an influence value of an individual cited insaid tagged content.

Additionally, the system further comprises:

-   -   an individual influence modeling module for calculating the        influence value of the individual cited in said tagged content        based on viral properties extracted from tagged content        identifying said individual, said influence value of the        individual characterizing a topical influence of the individual.

The commentor influence modeling module further comprises a commentorinfluence updating module for updating the influence of the commentor bycalculating the influence value of the commentor based on viralproperties extracted from the tagged content having a comment from thecommentor and influence values of outlets owned by said commentor.

The individual influence modeling module further comprises an individualinfluence updating module for updating the topical influence of theindividual by calculating the influence value of the individual based onviral properties extracted from tagged content identifying saidindividual and influence values of outlets owned by said individual.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the invention will now be described, by way of example,with reference to the accompanying drawings in which:

FIG. 1 illustrates a system architecture, in which the embodiments ofthe present invention have been implemented;

FIG. 2 illustrates a Content-to-Topic Matching block 150 of the systemof FIG. 1;

FIG. 3 shows a flowchart 300 illustrating the operation of theContent-to-Topic Matching block of FIG. 2;

FIG. 4 shows a block diagram for a system for a recursive determining ofinfluence of a commentor, individual, or a site according to theembodiment of the invention; and

FIG. 5 shows a flowchart illustrating the operation of the system ofFIG. 4.

DETAILED DESCRIPTION OF THE EMBODIMENTS OF THE INVENTION

Embodiments of the invention describe models for determining aninfluence of a commentor, an individual, a web-site, or site, whereinthe model for determining the influence of the site uses, recursively,an influence value for a commentor or an influence value for anindividual, or both, when the commentor or individual is present in thesite. The influence values for the commentor and individualcharacterize, respectively, the influence of the commentor andindividual. The influences are topically relevant, and can be aggregatedacross various forms of content. The influence of the commentor iscalculated from an influence model for the commentor which is based onaggregated viral properties across content having comments from thecommentor. The viral properties will be also referred to as herein asviral dynamics. Additionally, viral properties of social media outletsowned by the commentor, when available, are integrated into theinfluence model for the commentor. The influence of the individual iscalculated from an influence model for the individual, which is based onaggregated viral properties of the content citing the individual. Theinfluence model for the site hosting the topically-relevant content isbased on the extracted viral properties of the content. Furtherfine-tuning of the influence model for the site is provided byselectively integrating the influence value for the commentors andinfluence value for the individuals.

FIG. 1 illustrates architecture of a system 100, in which theembodiments of the present invention have been implemented. The system100 includes a Content-to-Topic Matching Module 150, an InfluenceCalculation & Viral Properties Extraction Module 160 and a RecursiveInfluence Modeling Module 110. The system 100 includes also Surfaced TopInfluential Sites data store 120, Surfaced Top Influential Commentorsdata store 130 and Surfaced Top Influential Individuals data store 140.

As shown in FIG. 1, the Recursive Influence Modeling Module 110 isconnected to the Content-to-Topic Matching Module 150. The RecursiveInfluence Modeling Module 110 is also connected to the InfluenceCalculation & Viral Properties Extraction Module 160 and to the datastores 120, 130 and 140. The Recursive Influence Modeling Module 110processes information received from the Content-to-Topic Matching Module150 and the Influence Calculation & Viral Properties Extraction Module160, and generates, as output, data related to top influencers.

Surfaced Top Influential Sites data store 120, Surfaced Top InfluentialCommentors data store 130 and Surfaced Top Influential Individuals datastore 140 contain, respectively, top influential sites, top influentialcommentors and top influential individuals. The data in the data stores120, 130 and 140 is generated from the Recursive Influence ModelingModule 110. The data stores 120, 130 and 140 are implemented asdatabases stored in a computer readable storage medium, e.g., computermemory, DVD, CD-ROM, accessible through a database query language. Otheralternative implementations can also be adopted to store and retrievethe stored data.

The system illustrated in FIG. 1 comprises a computer, having aprocessor and a computer readable storage medium having computerreadable instructions stored thereon for execution by the processor, andforming modules of the system of FIG. 1 described above. The computerreadable storage medium can be implemented as a computer memory, CD-ROM,DVD, floppy, hard drive or the like. The modules of the system of FIG.1, when executed by the computer, perform the steps of the method of theembodiment of the present invention.

As an example, each of the individual modules of the system 100 can beimplemented as an individual software module or agent running on acommon hardware platform. Other alternative implementations are possibleand are well known to the persons skilled in the art.

The Content-to-Topic Matching Module 150 of the system 100 matchesaccessed content to user-defined topics to produce tagged content. Thearchitecture and operation of this module will be described withreference to FIGS. 2 and 3 below.

The Influence Calculation & Viral Properties Extraction Module 160extracts viral properties from the tagged content and computes influencevalues based on user-defined influence weights. This module will belater described with reference to FIGS. 4 and 5. The viral properties orviral dynamics, and the tagged content are processed by the RecursiveInfluence Modeling Module 110 to generate a list of top influencers.

FIG. 2 shows a block diagram of the Content-to-Topic Matching Module 150of the system 100. The diagram 150 shows entities 220 such as anIndividual (Person), a Company or a named group or organization, whichmay have one or more outlets where they publish the content. A contentsite 210 is defined as a site hosting one or more outlets, through whichan entity or commentor regularly publishes some form of content orcomment. The content site 210 is accessible to an Internet Crawler 230,which is connected to a Topic Modeling/Classification Module 240. TheTopic Modeling/Classification Module 240 is also connected to a TopicContainer 250 from which it receives topic-related information. TheTopic Modeling/Classification Module 240 processes the topic-relatedinformation and the content retrieved by the Internet Crawler 230 tomatch the content to a defined topic. The matched content is then storedin a tagged content database 260.

The Topic Container 250 is a collection of words, phrases, along withthe necessary Boolean logic, which describes a subset of various socialmedia content, usually centered around a brand, name, field of study,market, concept, or product. FIG. 2 shows an exemplary content of theTopic Container 250 presented as a table identifying a topic profiledefined by a Topic_ID 253 and a collection of keywords and Booleanoperators 255 describing the Topic_ID 253. In this exemplary table, thetopic is identified as “Social media” and the keywords defining thetopic include, but are not limited to, ‘blogging’, ‘social media’,‘social networking’, twitter, Facebook, LinkedIn, ‘video sharing’, etc.,which describe the topic “Social Media”, as listed in the keywords andBoolean operators 255.

The Topic Modeling/Classification Module 240 defines a topic model,which is a trained text classification model, created by feeding a textclassifier, a labeled corpus of on-topic and not on-topic content. Theclassifier then gauges unlabelled data based on how closely it matchesthe trained topic model. Text or content classification methods are wellknown to those skilled in the art, and any suitable classification modelcan be used to classify and tag the content.

The operation of the Content-to-Topic Matching Module 150 will now bedescribed in more detail with reference to a flow chart 300 shown inFIG. 3. At step 310, a user defines a topic container encapsulating atopic profile 320 against which the retrieved content needs to bematched. The topic profile 320 is defined by the set of keywords andBoolean operators 255 and the Topic_ID 253 as shown in FIG. 2.

At step 340, social media content sites 330 are identified by crawlingthe Internet, followed by presenting the discovered content sites 350 toan analysis phase. All discovered social media content sites 350 arepassed through the analysis phase at step 360, where the content ismatched against the topic profile 320. If the content does not match thetopic profile 320 (exit “No” from step 360), it is disregarded in step380. If a match is found (exit “Yes” from step 360), the content is thentagged with the corresponding topic in step 370.

FIG. 4 shows a block diagram 400 for the system for recursivelydetermining the influence of a commentor, an individual, or a siteaccording to the embodiment of the invention. A set of Tagged Content460 is provided in connection with a Viral Properties Time SeriesExtractor 450. Each piece of content from the set of tagged content 460,as described above, is a content that matches the selected topic profile320 and is contained or hosted by a content site. Each piece of contentis part of an entity's outlet such as blog, twitter, streaming videochannel, micromedia or social networking outlet, etc.

The Viral Properties Time Series Extractor 450 extracts the viralproperties related to the set of tagged content 460 and stores them intime series so that the history of each viral property is recorded.Viral properties or viral dynamics are defined as the various socialmedia popularity metrics. Examples of viral properties include, but arenot limited to:

-   -   User engagement across topically relevant posts, wherein the        engagement is measured by the length of the commenting threads        and the number of unique commentors;    -   Average Comment count across topically relevant posts;    -   Average unique commentor count across topically relevant posts;    -   Cited individual count,    -   Inbound links across topically relevant posts;    -   Blog subscribers across all posts;    -   Average Social bookmarks across all topically relevant posts;    -   Average Social news votes and buries across all topically        relevant posts;    -   Total Count of topically relevant posts; and    -   Total Count of appearance of Individuals names across all posts.

Other viral properties include breadth of reply, views, bookmarks,votes, buries, favorites, awards, acceleration, momentum, subscriptioncounts, replies, spoofs, ratings, friends, followers, posts, andupdates.

An Influence Determination Module 440 is connected to the ViralProperties Time Series Extractor 450 for determining the influence ofeach piece of content of the set of tagged content 460. In Determiningthe influence of a piece of content, the Influence

Determination Module 440 receives user-defined influence weights foreach collected viral properties and applies a mathematical model such asa linear combination model involving both the weights and the viralproperties.

The Influence Determination Module 440, in this embodiment is alsoresponsible for the determination of the influence of an outlet asdescribed in the co-pending U.S. application Ser. No. 12/174,345 filedJul. 18, 2008 entitled “Method And System For Determining TopicalOn-Line Influence Of An Entity” cited above.

A Topic-Commentor Influence Modeling Module 410 receives informationrelated to the names of Unique Commentors and links from the ViralProperties Time Series Extractor 450 extracted from the tagged content460. The links relate to hypertext or other web links that point back tothe outlets such as blog, twitter or macromedia owned by the uniqueCommentors. The module 410 creates a topic-commentor influence modelbased on the viral properties across all content where the commentor hasa comment, and determines the topical influence of the commentorcharacterized by an influence value of the commentor.

The module 410 includes also an update module (CI_Updating Module 411)for updating the commentor influence value. When the commentor has hisown outlet, the CI_Updating Module 411 updates the influence value ofthe commentor based on an influence value of the outlets owned by thecommentor and calculated by the Influence Determination Module 440.

A Recursive Site Influence Modeling module 420 is provided to processthe set of tagged-content 460 and identify sites hosting the set oftagged content 460. The module 420 determines the influence of a site,characterized by a site influence value, from a linear combination modelbased on the content influences generated by the Influence DeterminationModule 440 by summing the content influences across all topicallyrelevant content hosted by the site. Alternatively, the site influencecan be computed by aggregating the extracted viral properties across alltopically relevant content hosted by the site. The aggregation ofextracted viral properties relates to the summing of all individualextracted viral properties. For example, if Content_1 has two types ofviral properties with the following values: VP_C1_1 and VP_C1_2; andContent_2 has the same two types of viral properties with the followingvalues: VP_C2_1 and VP_C2_2; then the aggregated viral properties wouldbe: (VP_C1_1+VP_C2_1) and (VP_C1_2+VP_C2_2).

The Recursive Site Influence Modeling module 420 includes a SI_Updatingmodule 412 for recursively fine-tuning the site influence model byincluding the influence value of the commentor from the topic-commentorinfluence model as well as an influence value for an individualcalculated from a topic-individual influence model. In this fine-tuningoperation, the site-influence model uses the influence of commentorscommenting on the site and the influence of individuals named on thesite to recursively update the model and determine the site influence.This process will be described hereinafter with reference to FIG. 5.

A Topic-Individual Influence Modeling Module 430 identifies individualsnamed in the content and creates the topic-individual influence modelbased on an aggregation of viral dynamics of content citing theindividual. The Topic-Individual Influence Modeling Module 430 furtherincludes an I_Updating module 413 for updating the influence of theindividual characterized by the influence value of the individual usingrespective influence values of the outlets owned by the individual, whenavailable. The operation of the Topic-Individual Influence ModelingModule 430 will be described in more details in reference to FIG. 5.

FIG. 5 shows a flowchart 500 illustrating the operation of the system400 of FIG. 4.

In the embodiment of the invention, each piece of content 505 thatmatches the topic profile 320 is scheduled to have its viral propertiesextracted at step 510 on a regular schedule, and stored in time seriesso that the history of each viral property is recorded. The timeschedule used for extracting the viral properties changes as therecorded viral property values are analyzed. For instance, if uponchecking the viral properties for a blog post on a 3 hour schedule, itis determined that the number of new comments has exploded, then, theschedule will be altered to ensure that the viral properties are checkedmore frequently. Conversely, if the comment count has changed little ornot at all, the schedule may be changed to half the frequency, down toevery 6 hours. Conveniently, different viral properties may have same ordifferent time extraction schedule.

The extracted viral properties are used at “Determine Influence Based onViral Properties of Content”, step 520, to determine the influence ofeach piece of content 505 based on the collected viral properties. Atstep 520, the influence values of outlets are also calculated asdisclosed in the above-mentioned patent application cited above.

As an example, the influence value for the piece of content 505 can becalculated according to a linear combination model of the viralproperties weighted with respective weights applied to each of the viralproperties:

CalculatedContentInfluence=(Weight1*VP1)+(Weight2*VP2)+(Weight3*VP3)+(Weight4*VP4)+(Weight5*VP5)+(Weight6*VP6)++(Weightn*VPn),

where Weight1-Weightn are respective weight factors defining therelevant contribution of various viral properties (VP1-VPn) into thetopical on-line influence for this content, and the topical on-lineinfluence is represented by a value conveniently normalized to a scaleof 0-100.

In the embodiment of the present invention, a user is responsible foradjusting the weights (i.e. Weight1-Weightn) for various viralproperties used in the model above to reflect the viral propertieswhich, in user's opinion, are most suitable for the business goals theuser or his clients have set forth. The weight for each type of theoutlet is also user defined and is entered at “User-Defined InfluenceCalculation Weights” step 525.

It is assumed that same default weights can be applicable to an averageuser. The weights adjusted by a user are saved on a per topic basis,allowing for a different topic to have a different weighting system toalign with potentially different business goals.

At “Extract Unique Commentor Names and Links”, step 530, names ofcommentors commenting on the piece of content 505 are extracted alongwith any link back to the outlets owned by the commentor. As an example,Robert Scobble may be a commentor on the piece of content 505, and hemay also be providing links to his own outlets such as blog, twittersite, Facebook profile, and streaming video channel(Kyte.tv/RobertScobble).

At step 540, the Commentor Influence model is created from theaggregation of the viral properties across all content having a commentfrom the selected commentor.

The influence value computed from Commentor Influence model, termedherein as Commentor_AggregatedViralsInfluence, is calculated accordingto a linear combination model of the extracted viral properties weightedwith respective weights applied to each of the extracted viralproperties as shown in the following model:

Commentor_AggregatedViralsInfluence=(Weight_11*AVP1)+(Weight_22*AVP2)+(Weight_33*AVP3)+. . . (Weight_nn*AVPn),

where Weight_11-Weight_nn are respective weight factors definingrelevant contribution of various viral properties into the modeling ofthe Commentor_AggregatedViralsInfluence, and where AVP1-AVPn arerespective Aggregated Viral Properties extracted across all pieces ofcontent 505 containing a comment from the selected commentor, namelyRobert Scobble in this example.

The Commentor Influence model, according to the embodiment of thepresent invention, is further updated by the CI_Updating Module 411based on the influence values of all outlets owned by the commentorRobert Scobble.

Considering that the commentor, Robert Scobble, has his own outlets,namely a blog, video channel, MicroMedia channel, social network andsocial news page and image sharing page, The Commentor Influence modelcan then be updated according to a linear combination model of thetopical influence values of the outlets owned by Robert Scobble and thetopical influence value of the commentor calculated above asCommentor_AggregatedViralsInfluence as shown in the following model:

CommentorInfluence=((Weight000*Commentor_AggregatedViralsInfluence)+(Weight111*Commentor_CalculatedBlogInfluence)+(Weight222*Commentor_CalculatedVideoChannelInfluence)+(Weight333*Commentor_CalculatedMicroMediaInfluence)+(Weight444*Commentor_CalculatedSocialNewsInfluence)+(Weight555*Commentor_CalculatedSocialNetworkInfluence)+(Weight666*Commentor_CaculatedImageSharingInfluence))/7.

where Weight111-Weight666 are respective weight factors assigned to thevarious forms of outlets, Weight000 is the weight factor for theCommentor_AggregatedViralsInfluence value into the final Commentorinfluence value, and where Commentor_CalculatedBlogInfluence,Commentor_CalculatedVideoChannelInfluence,Commentor_CalculatedMicroMediaInfluence,Commentor_CalculatedSocialNewsInfluence,Commentor_CalculatedSocialNetworkInfluence, andCommentor_CaculatedImageSharingInfluence

are respective influence values of the various outlets owned by RobertScobble calculated at step 520 as disclosed in the above-mentionedpatent application incorporated herein by reference.

An influence value characterizing the Commentor_CalculatedBlogInfluencecan be calculated using a linear combination model as follows:

Commentor_CalculatedBlogInfluence=(Weight1*BlogEngagement)+(Weight2*Averagecomment Count)+(Weight3*Average Unique Commentor Count)+(Weight4*InboundLinks)+(Weight5*BlogSubscribers)+(Weight6*Bookmarks)+(Weight7*Votes)+(Weight8*Count ofTopically relevant posts),

where Weight1-Weight8 are respective weight factors defining therelevant contribution of various viral properties into the topicalon-line influence for Robert Scobble blog.

Likewise the influence value characterizing theCommentor_CalculatedVideoChannelInfluence can be calculated as follows:

Commentor_CalculatedVideoChannelInfluence=(Weight11*Average ConcurrentViewership)+(Weight12*Total Views)+(Weight13*InboundLinks)+(Weight14*Engagement)+(Weight15*Average CommentCount)+(Weight16*Unique Commentor Count)+(Weight17*Count of TopicallyRelevant Posts)

where Weight11-Weight17 are respective weight factors defining therelevant contribution of various viral properties into the topicalon-line influence of this video channel outlet.

Using the linear combination models above, the influence valuecharacterizing the topical on-line influence of all the outlets can becalculated by taking into account specific viral properties that aremeasurable in the selected outlet.

As each viral property value is stored in time series, the topicalon-line influence of all commentors can be determined for any time ortime periods, and top influential commentors can be identified orsurfaced at step 545.

In one embodiment of the invention, topical on-line influences forindividuals are also determined. As shown in the flowchart 500, at step550, names of individuals cited in all pieces of content 505 areextracted, and their viral properties are aggregated around eachindividual to create a topic-Individual influence model (step 560). Asan example, an individual named Cesc may be cited in one or more piecesof content 505. In this example, the topical on-line influence of Cesccan be determined by calculating the influence value of the individual,(i.e. Cesc) based on a linear combination model. The model can be basedon aggregated viral dynamics of all the content where Cesc is cited, andapplying user-defined weights to the aggregated viral dynamics, asfollows:

Individual_AggregatedViralsInfluence=(Weight_01*AVD_01)+(Weight_02*AVD_02)+(Weight_03*AVD-03)+. . . (Weight_0n*AVD_0n)

where Weight_01-Weight_0n are respective weight factors definingrelevant contribution of various viral dynamics into the influence ofthe individual, and where AVD_01-AVD_0n are respective Aggregated ViralDynamics extracted across all pieces of content 505 mentioning theselected individual (i.e. Cesc).

Furthermore, for individuals who have their own social media outlet suchas blog, video channel or other social media page, the topic-individualinfluence model can integrate the influence of those outlets using theI_Updating module 413 of the Topic-Individual Influence Modeling Module430 according to the following linear combination model:

IndividualInfluence=((Weight_000*Individual_AggregatedViralsInfluence)+(Weight_111*Individual_CalculatedBlogInfluence)+(Weight_222*Individual_CalculatedVideoChannelInfluence)+(Weight_333*Individual_CalculatedMicroMediaInfluence)+(Weight_444*Individual_CalculatedSocialNewsInfluence)+(Weight_555*Individual_CalculatedSocialNetworkInfluence)+(Weight_666*Individual_CaculatedlmageSharingInfluence))/7.

where Weight_111-Weight_666 are respective weight factors definingrelevant contribution of various forms of outlets, and Weight_000 is theweight factor for the Individual_AggregatedViralsInfluence value intothe final Individual influence value.

Using this method, all individuals can be assigned an associatedinfluence value, followed by identifying top individual influencers atstep 570.

At step 580, a site influence model referred also as topic-siteinfluence model is recursively created for each content site hosting oneor more content matching the selected topic profile based on theaggregation of the viral properties for all pieces of content 505 withineach site and an influence weight (Site_type_weight) according to thetype of outlet hosted by the site (e.g. blog, twitter, social mediasite, etc). When a content site includes content, in which aninfluential commentor is present, the SI_updating module 412 fine-tunesthe site influence model by recursively applying the influence value ofthe commentor in the site influence model. In this embodiment, theinfluence value of the commentor is added to the site influence model ina weighted manner. The influence value of a site named S1 is calculatedaccording to the following linear combination model:

CalculatedSiteInfluence_S1=Site_type_weight*(W0*S1_AVP0+ . . .Wn*S1_AVPn)+(CW0*CommentorInfluence_0+ . . . CWk*Commentorinfluence_k)

where Site_type_weight is the weight defined for the type of site (blogsite, video channel provider site, twitter type of site, etc); W0 . . .Wn are respective weights for the aggregate viral properties S1_AVP0 . .. S1_AVPn collected on all content of the site S1, and CW0 . . . CWk arerespective weights applied to the influence values of the commentorsCommentorInfluence_0 . . . CWn*CommentorInfluence_k. The weights CWi aswell as the number of commentors can be user-defined and set accordingto business objectives, or a threshold of influence. Other criteria forsetting these values can as well be adopted.

The above noted model is exemplary, it is contemplated that other modelscan as well be used to determine the influence value of a site.

According to another embodiment, one or more top individual influencerscan be included in the site influence model described with reference tostep 580. Thus, a modified model for calculating the influence value ofthe site becomes as follows:

CalculatedSiteInfluence_S1=Site_type_weight*(W0*S1_AVP0+ . . .Wn*S1_AVPn)+(CW0*CommentorInfluence_0+ . . .CWk*Commentorinfluence_k)+(IW0*IndividualInfluence_0+ . . .IW1*IndividualInfluence_1)

where IW0 . . . IW1 are respective weights applied to the influencevalues of individuals IndividualInfluence_ . . . IndividualInfluence_1.

By iterating the determination of the site influence characterized bythe site influence value across all sites having content that matchesthe topic container, top Site Influencers can be identified (step 585).

The embodiments of the present invention provide numerous advantages,most importantly, to public relation professionals to make preemptivemarketing decisions that are not available today. Within a selectedtopic, the present invention allows marketers to compare two differentsites, each hosting a set of outlets, and determine which one has themost topical on-line influence, and therefore has more potential toinduce a successful advertising campaign.

As mentioned above, the system of the present invention have beenimplemented as a general purpose or specialized computer having a CPUand a memory storing computer code instructions to be performed by theCPU, which form the modules of the system perform as shown in FIGS. 1and 4 described above, and, when executed, perform the steps of themethod described above.

A computer readable medium is also provided, e.g., CD-ROM, DVD, flashmemory, floppy or the like, wherein the computer code instructions arestored, for executing by a computer to perform the steps of the methoddescribed above.

Thus, improved methods and system for determining topical on-lineinfluence of a site, commentor, and an individual have been provided.

1. A computer-implemented method performed by a computer incommunication with a server via network to access a web-site hosted bythe server, the computer-implemented method comprising steps of: using aprocessor of the computer to match content within the web-site with aselected topic and to tag matching content to generate tagged content;extracting, with the processor, viral properties for the tagged content;aggregating, with the processor, the viral properties of the taggedcontent citing an individual in the tagged content to form aggregatedviral properties of the tagged content citing the individual; andcomputing, with the processor, a topical influence value of theindividual based on the aggregated viral properties of the taggedcontent citing the individual.
 2. The computer-implemented method ofclaim 1, further comprising the steps of: aggregating, with theprocessor, the viral properties for the tagged content from the web-siteto form aggregated viral properties of the tagged content from theweb-site; and computing, with the processor, a topical influence valueof the web-site based on a combination of the aggregated viralproperties of the tagged content from the web-site, and the topicalinfluence value of the individual cited in the tagged content.
 3. Thecomputer-implemented method of claim 2, further comprising: repeatingthe steps of matching and tagging, extracting, identifying, aggregatingand computing, the repeating of the computing step including: updatingthe topical influence value of the individual to generate an updatedtopical influence value of the individual; and updating the topicalinfluence value of the web-site to generate an updated topical influencevalue of the web-site by integrating the updated topical influence valueof the individual in the computation of the updated topical influencevalue of the web-site.
 4. The computer-implemented method of claim 1,wherein the step of computing the topical influence value of theindividual, further comprises: identifying an outlet owned by theindividual; calculating a topical influence value of the outlet owned bythe individual based on a combination of viral properties extracted fromthe outlet owned by the individual; and updating the topical influencevalue of the individual based on a combination of the aggregated viralproperties of the tagged content citing the individual and the topicalinfluence value of the outlet owned by the individual.
 5. Thecomputer-implemented method as described in claim 1, further comprising:iterating the steps of matching and tagging, extracting, identifying,aggregating and computing for a plurality of individuals; andidentifying top influential individuals from the plurality ofindividuals based on the topical influence values for each of theplurality of individuals.
 6. The computer-implemented method asdescribed in claim 1, wherein the step of extracting viral propertiesfor the tagged content comprises: collecting values of the viralproperties at predetermined time intervals; and storing the collectedvalues in respective time series.
 7. The computer-implemented method ofclaim 1, wherein the viral properties are selected from the groupconsisting of: user engagement value; cited individual count, inboundlinks; sub scribers; average Social bookmarks; average Social newsvotes; buries; total count of posts; and total count of appearance ofindividuals names across all posts.
 8. A method performed by a computerfor in communication with a server via network to access a web-sitehosted by the server, the method comprising the steps of: using aprocessor of the computer to match content within the web-site with aselected topic and to tag matching content to generate tagged content;extracting, with the processor, viral properties for the tagged content;aggregating, with the processor, the viral properties across all taggedcontent contained within the web-site including the viral properties ofthe tagged content citing the individual in the tagged content to formaggregated viral properties of the tagged content citing the individual;computing, with the processor, the topical influence value of theindividual based on a combination of the aggregated viral properties ofthe tagged content citing the individual; and repeating the steps ofmatching and tagging, extracting, identifying, aggregating andcomputing, the repeating of the computing step including: updating thetopical influence value of the individual to generate an updated topicalinfluence value of the individual.
 9. The method of claim 8, furthercomprising: iterating the steps of matching and tagging, extracting,identifying, aggregating and computing for a plurality of individuals;and identifying top influential individuals from the plurality ofindividuals based on the topical influence values for each of theplurality of individuals.
 10. The method of claim 8, wherein the step ofcomputing the topical influence value of the individual comprises:identifying an outlet owned by the individual; determining an influenceof the outlet characterized by a topical influence value of the outlet;and calculating the topical influence value of the individual byincluding the topical influence value of the outlet in the combinationof the aggregated viral properties of the tagged content citing theindividual.
 11. The method of claim 8, further comprising: aggregating,with the processor, the viral properties for the tagged content from theweb-site to form aggregated viral properties of the tagged content fromthe web-site; computing, with the processor, the topical influence valueof the web-site based on a combination of the aggregated viralproperties of the tagged content from the web-site, and the topicalinfluence value of the individual cited in the tagged content, thetopical influence value of the web-site characterizing the topicalon-line influence of the web-site; and repeating the steps of matchingand tagging, extracting, identifying, aggregating and computing, therepeating of the computing step including: updating the topicalinfluence value of the individual to generate an updated topicalinfluence value of the individual; and updating the topical influencevalue of the web-site to generate an updated topical influence value ofthe web-site by integrating the updated topical influence value of theindividual in the computation of the updated topical influence value ofthe web-site.
 12. The method of claim 11, further comprising: iteratingthe steps of matching and tagging, extracting, identifying, aggregatingand computing for a plurality of web-sites; and identifying topinfluential sites based on the topical influence values for each of theplurality of web-sites.
 13. A non-transitory computer readable medium,comprising computer code instructions stored thereon, which, whenexecuted by a computer coupled to a network and in communication with aserver via the network to access a web-site hosted by the server, causethe computer to perform the steps of: matching content within theweb-site with a selected topic and tagging matching content to generatetagged content from the web-site; extracting viral properties for thetagged content; aggregating the viral properties of the tagged contentciting an individual in the tagged content to form aggregated viralproperties of the tagged content from the web-site citing theindividual; and computing a topical influence value of the individualbased on a combination of the aggregated viral properties of the taggedcontent citing the individual.
 14. The non-transitory computer readablemedium of claim 13, comprising the computer code instructions storedthereon, which, when executed by the computer, further cause thecomputer to perform the steps of: aggregating the viral properties forthe tagged content from the web-site to form aggregated viral propertiesof the tagged content from the web-site; and computing a topicalinfluence value of the web-site based on a combination of the aggregatedviral properties of the tagged content from the web-site, and thetopical influence value of the individual cited in the tagged contentcontained within the web-site.
 15. The non-transitory computer readablemedium of claim 14, comprising the computer code instructions storedthereon, which, when executed by the computer, further cause thecomputer to perform the steps of: repeating the steps of matching andtagging, extracting, identifying, aggregating and computing, therepeating of the computing step including: updating the topicalinfluence value of the individual to generate an updated topicalinfluence value of the individual; and updating the topical influencevalue of the web-site to generate an updated topical influence value ofthe web-site by integrating the updated topical influence value of theindividual in the computation of the updated topical influence value ofthe web-site.
 16. The non-transitory computer readable medium of claim12, comprising the computer code instructions stored thereon, which,when executed by the computer, further cause the computer to perform thesteps of: identifying an outlet owned by the individual; calculating atopical influence value of the outlet owned by the individual based on acombination of viral properties extracted from the outlet owned by theindividual; and updating the topical influence value of the individualbased on a combination of the aggregated viral properties of the taggedcontent citing the individual and the topical influence value of theoutlet owned by the individual.
 17. The non-transitory computer readablemedium of claim 12, comprising the computer code instructions storedthereon, which, when executed by the computer, further cause thecomputer to perform the steps of: iterating the steps of matching andtagging, extracting, identifying, aggregating and computing for aplurality of individuals; and identifying top influential individualsfrom the plurality of individuals based on the topical influence valuesfor each of the plurality of individuals.
 18. A system, comprising: anetwork; a server coupled to the network, wherein the server hosts theweb-site; a computer coupled to the network and in communication withthe server to access the web-site, the computer comprising: a processor;and a computer readable storage medium having computer readableinstructions stored thereon for execution by the processor, forming thefollowing modules: a content-to-topic matching module for matchingcontent within the web-site with a selected topic and tagging matchingcontent to generate tagged content from the web-site; a viral propertiesextraction module for: extracting viral properties of the taggedcontent; and aggregating the viral properties of the tagged contentciting the individual to form aggregated viral properties of the taggedcontent citing the individual; an individual influence modeling modulefor computing, based on a combination of the aggregated viral propertiesextracted from the tagged content citing the individual, a topicalinfluence value of the individual cited in the tagged content; and adatabase comprising: a top influential individuals data store forstoring the topical influence value.
 19. The system of claim 18, whereinthe computer readable storage medium having computer readableinstructions stored thereon for execution by the processor furtherforms: a site influence modeling module for computing the topicalinfluence value of the web-site based on a combination of the aggregatedviral properties of the tagged content within the web-site, and thetopical influence value of the individual cited in the tagged content,and wherein the database further comprises: a top influential sites datastore for storing the topical influence value of the web-site.
 20. Thesystem of claim 18, wherein the individual influence modeling modulefurther comprises: an individual influence updating module for updatingthe topical influence of the individual by calculating the topicalinfluence value of the individual based on viral properties extractedfrom tagged content identifying the individual and topical influencevalues of outlets owned by the individual.